RU2599277C1 - Computed tomography system for inspection and corresponding method - Google Patents

Abstract

FIELD: safety.

SUBSTANCE: invention can be used for inspection of the object by computed tomography (CT). Core of the invention is that disclosed are a CT system for inspection and a corresponding method. Method involves: reading data of the examination for the investigated object; inserting at least one fictive three-dimensional (3D) image (FTI) of a dangerous object into the 3D examination image for the investigated object, which is obtained from the examination data; receiving a selection of at least one region on the 3D image of the examination, including the 3D FTI, or at least one region on a two-dimensional (2D) image of the examination, including the 2D FTI corresponding to the 3D FTI, herewith the 2D image of the examination is obtained from the 3D image of the examination or is obtained from the examination data; and output in response to the said selection a feedback on the 3D image of the examination, including at least one 3D FTI.

EFFECT: technical result is the possibility of quick marking a suspicious object on the CT-image determining by the feedback an image of the dangerous object.

16 cl, 21 dwg

Description

Cross reference to related application

[0001] This application claims priority based on Chinese Patent Application No. 201410290133.7, filed June 25, 2014, entitled "CT SYSTEM FOR SECURITY CHECK AND METHOD THEREOF", which is incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

[0002] The present invention relates to an inspection, and in particular, to a computerized tomography (CT) system for inspection and to an appropriate method.

State of the art

[0003] Multi-energy X-ray inspection systems belong to a new type of inspection system developed on the basis of monoenergy X-ray inspection systems. A multi-energy X-ray inspection system can provide not only the shape and contents of the subject, but also information reflecting the equivalent atomic number of the subject to identify whether the subject is an organic substance or inorganic substance, and display the subject in different colors on a color monitor for easy recognition the operator.

[0004] In the field of inspection, an important need is the projection of an image of a dangerous object (TIP). The so-called TIP refers to those images of dangerous objects that are accumulated in advance, inserted into the image, for example, baggage or parcels, that is, fictitious images of dangerous objects (FTI) are inserted. This plays an important role in training inspectors to inspect and evaluate the performance of these inspectors. With regard to two-dimensional (2D) TIP for X-ray inspection systems, there are proven solutions and a number of applications. However, with respect to three-dimensional (3D) TIPs for CT screening, there is currently no manufacturer that provides such functionality.

Disclosure of invention

[0005] Given one or more technical problems in the prior art, the present invention provides a CT scan system and an appropriate method by which a user can quickly mark a suspicious item in a CT image and provide feedback on whether an FTI image is included .

[0006] In accordance with an aspect of the present invention, there is provided a screening method in a computed tomography (CT) system, comprising the steps of: reading survey data for a subject to be examined; inserting at least one three-dimensional (3D) fictitious image of a dangerous object (FTI) into the 3D image of the examination for the subject being examined, wherein the 3D image of the examination is obtained from the survey data; receiving a selection of at least one region in the 3D image of the survey, including 3D FTI, or at least one region in the two-dimensional (2D) image of the survey, including a 2D FTI corresponding to 3D FTI, wherein the 2D image of the survey is obtained from 3D images of the survey or obtained from survey data; and issuing, in response to said selection of feedback, a 3D survey image including at least one 3D FTI.

[0007] According to some embodiments, the step of receiving a selection of at least one area in a 3D survey image including 3D FTI, or at least one area in a 2D survey image including 2D FTI corresponding to 3D FTI , contains: receiving the coordinates of a portion of the 3D survey image or 2D survey image associated with the selection.

[0008] In accordance with some embodiments, the step of issuing in response to a feedback selection of a 3D survey image including at least one 3D FTI comprises at least one of: assessing whether at least one selected area at least one 3D FTI; displaying a dialog to confirm that the 3D image of the examination includes at least one 3D FTI; issuing a textual indication in the interface to confirm that the 3D image of the examination includes at least one 3D FTI; highlighting a portion of the 3D survey image or 2D survey image associated with the selection; marks of a portion of the 3D survey image or 2D survey image associated with the selection; and filling out a portion of the 3D survey image or 2D survey image associated with the selection, a specific color or pattern.

[0009] In accordance with some embodiments, at least one spatial parameter of the subject is calculated in accordance with the survey data, and at least one 3D FTI is inserted into the 3D image of the survey for the subject based on the spatial characteristic.

[0010] According to some embodiments, the spatial characteristic parameter refers to at least one of the position, size, and orientation of the inserted 3D FTI.

[0011] In accordance with some embodiments, selecting at least one area comprises selecting a portion of the displayed 3D survey image at a certain viewing angle.

[0012] In accordance with some embodiments, when 3D visualizing a 3D image of an examination, information is provided about a point cloud characterizing the subject to be examined, and the step of issuing in response to a feedback selection of a 3D image of the examination including at least one 3D FTI, contains: obtaining a sequence of clusters of information about the cloud of points of various objects in the subject being examined by separation; determining at least one selected area from a sequence of clusters of information about a cloud of points of different objects based on a given criterion; and evaluating whether at least one selected area includes at least one 3D FTI.

[0013] In accordance with some embodiments, selecting at least one area comprises selecting a portion of a displayed 3D survey image from a plurality of viewing angles.

[0014] In accordance with some embodiments, selecting at least one area comprises selecting a portion of the displayed 3D survey image at two different viewing angles, the two different viewing angles being substantially orthogonal to each other, the transparent areas are removed from the survey data to obtain a hierarchical the bounding rectangle from opaque areas in the survey data, and then the scene depth for the hierarchical bounding rectangle is visualized to get a depth map of n the far side and the depth map on the back side, and the stage of issuing in response to the choice of feedback about the 3D image of the survey, which includes at least one 3D FTI, contains: performing a search, respectively, on the depth map on the front side and the depth map on the back side in accordance with a user-selected area at a first viewing angle to form a first bounding box; performing ray tracking when the first bounding box is formed as a texture carrier; performing a search, respectively, on the depth map on the front side and the depth map on the back side in accordance with a user-selected region at a second viewing angle substantially orthogonal to the first viewing angle to form a second bounding box; performing a Boolean intersection operation on the first bounding box and the second bounding box in the image space to obtain a marked area in 3D space as at least one selected area; and evaluating whether at least one selected area includes at least one 3D FTI.

[0015] According to some embodiments, the step of inserting at least one 3D FTI into the 3D image of the exam for the subject to be examined comprises: splitting the 3D image of the exam to obtain a plurality of fragments of a 3D image of the subject to be examined; calculating the distances between the multiple fragments of the 3D image and their positions; and inserting a 3D FTI based on calculated distances and positions.

[0016] In accordance with some embodiments, the step of inserting at least one 3D FTI into the 3D image of the exam for the subject contains: determining transparent parts and opaque parts in the volume data of the subject based on the voxel opacity values; determination of the positions and sizes of the boundaries of the inspected object from opaque parts in volume data; identification of possible insertion positions in transparent areas in the range of borders; and selecting at least one position from the possible insertion positions in accordance with a predetermined criterion in order to insert at least one 3D FTI in at least one selected position.

[0017] In accordance with some embodiments, the step of inserting at least one 3D FTI into a 3D survey image for the subject to be examined comprises: removing a background image from the 2D survey image to obtain a main 2D image; determination of the 2D position of the insert 2D FTI in the main 2D image; determining the position of the 3D FTI in the 3D image of the survey in the depth direction in the 2D position of the insert; and inserting at least one 3D FTI at a specific position.

[0018] According to some embodiments, the method further comprises: inserting a 2D FTI corresponding to at least one 3D FTI into the 2D image of the exam for the subject being examined.

[0019] In accordance with an additional aspect of the present invention, there is provided a computerized tomography (CT) system for inspection, comprising: a CT scan device configured to obtain examination data for an object to be examined; a storage device configured to store survey data; a display device configured to display a three-dimensional (3D) image of the examination and / or two-dimensional (2D) image of the examination for the subject being examined, wherein the 3D image of the examination is obtained from the examination data, and the 2D image of the examination is obtained from the 3D image of the examination or examination data ; a data processing device configured to insert at least one fictitious 3D image of a dangerous object (FTI) into a 3D image of the examination for the subject being examined; and an input device configured to receive a selection of at least one region in the 3D survey image including 3D FTI, or at least one region in the 2D survey image including 2D FTI corresponding to 3D FTI, the device the data processing is further configured to provide, in response to a choice, feedback on the 3D image of the survey, which includes at least one 3D FTI.

[0020] According to some embodiments, the data processing device is further configured to calculate at least one spatial parameter of the subject to be examined in accordance with the survey data and to insert at least one 3D FTI into the 3D image of the survey for the subject based on spatial parameter parameter.

[0021] According to some embodiments, the spatial characteristic parameter refers to at least one of the position, size, and orientation of the inserted 3D FTI.

[0022] In accordance with a further aspect of the present invention, there is provided a method for marking a suspicious item in a CT scan system, comprising the steps of: removing transparent areas from CT data obtained by the CT scan system to obtain a hierarchical bounding box from opaque areas in CT data; visualizing the scene depth for the hierarchical bounding box to obtain a depth map on the front side and a depth map on the back side; performing a search, respectively, on the depth map on the front side and the depth map on the back side, using the marks made by the user, in the direction of the line of sight to form a first bounding box; performing ray tracking when the first bounding box is formed as a texture carrier; performing a search respectively on the depth map on the front side and the depth map on the back side, using the marks made by the user, in a direction orthogonal to the direction of the line of sight to form a second bounding box; performing a Boolean intersection operation on the first bounding box and the second bounding box in the image space to obtain a marked area in 3D space; and displaying the marked area in 3D space together with CT data.

[0023] In accordance with some embodiments, the step of removing transparent areas comprises: creating a CT data sample in the direction of the line of sight; performing volumetric visualization integration over a segment between each two sample points using a lookup table for preliminary integration based on opacity to obtain opacity corresponding to that segment; and removing transparent regions by subdivision using the octree encoding algorithm to obtain a hierarchical bounding box corresponding to opaque data regions.

[0024] According to some embodiments, the step of visualizing the depth of a scene comprises: removing a section with a relatively large depth value to obtain a depth map on the front side; and removing a cross section with a relatively small depth value to obtain a depth map on the back side.

[0025] According to some embodiments, the first bounding rectangle and the second bounding rectangle are bounding rectangles in any direction.

[0026] In accordance with some embodiments, a marked area in 3D space is displayed together with CT data using a space-based transform function.

[0027] In accordance with an additional aspect of the present invention, there is provided an apparatus for marking a suspicious item in a CT scan system, comprising: means for removing transparent areas from CT data obtained by the CT scan system to obtain a hierarchical bounding box from opaque areas in CT data ; means for visualizing a scene depth for a hierarchical bounding box to obtain a depth map on the front side and a depth map on the back side; means for performing a search, respectively, on the depth map on the front side and the depth map on the back side, using the marks made by the user, in the direction of the line of sight to form a first bounding box; means for performing ray tracking with the first bounding box formed as a texture carrier; means for performing a search, respectively, on the depth map on the front side and the depth map on the back side, using the marks made by the user, in a direction orthogonal to the direction of the line of sight to form a second bounding box; means for performing the Boolean intersection operation on the first bounding box and the second bounding box in the image space to obtain a marked area in 3D space; and means for displaying the marked area in 3D space in conjunction with CT data.

[0028] According to some embodiments, the means for removing transparent areas comprises: means for creating a CT data sample in the direction of the line of sight; means for performing the integration of volumetric visualization over the segment between each two sample points, using the method with the search table, to obtain the opacity corresponding to that segment; and means for removing transparent regions by subdivision using the octree encoding algorithm to obtain a hierarchical bounding box.

[0029] According to some embodiments, the means for visualizing the depth of the scene comprises: means for removing a section with a relatively large depth value to obtain a depth map on the front side; and means for removing a cross section with a relatively small depth value to obtain a depth map on the back side.

[0030] Using the above technical solution, it is convenient for the user to quickly mark a suspicious item in the CT image, and feedback is provided on whether FTI is present.

Brief Description of the Drawings

[0031] For a better understanding of the present invention, the present invention will be described in detail in accordance with the accompanying drawings, in which:

[0032] FIG. 1 is a block diagram illustrating a CT examination system in accordance with an embodiment of the present invention;

[0033] FIG. 2 is a structural block diagram illustrating a computer data processing device shown in FIG. one;

[0034] FIG. 3 is a structural block diagram illustrating a controller in accordance with an embodiment of the present invention;

[0035] FIG. 4A is a flowchart illustrating a method in an inspection system in accordance with an embodiment of the present invention;

[0036] FIG. 4B is a flowchart illustrating a method for marking a suspicious item in a CT system in accordance with an embodiment of the present invention;

[0037] FIG. 5 is a schematic diagram illustrating an octet division algorithm;

[0038] FIG. 6 is a diagram illustrating a hierarchical bounding box obtained using an octree division algorithm in accordance with an embodiment of the present invention;

[0039] FIG. 7 is a diagram illustrating a depth map on the front side in accordance with an embodiment of the present invention;

[0040] FIG. 8 is a diagram illustrating a depth map on the rear side in accordance with an embodiment of the present invention;

[0041] FIG. 9 is a diagram illustrating a ray tracking process in accordance with an embodiment of the present invention;

[0042] FIG. 10 is a diagram illustrating user-made marks in accordance with an embodiment of the present invention;

[0043] FIG. 11 is a diagram illustrating a search process on a front side and a back side using marks from a user;

[0044] FIG. 12 is a diagram illustrating a result of a search on the front side and the back side in accordance with an embodiment of the present invention;

[0045] FIG. 13 is a diagram illustrating an oriented bounding box (OBB) with columns of marked points in accordance with an embodiment of the present invention;

[0046] FIG. 14 is a diagram illustrating a new ray tracking range updated based on a previous mark result;

[0047] FIG. 15 is a diagram illustrating a result of marking a second time in the orthogonal direction in accordance with an embodiment of the present invention;

[0048] FIG. 16 illustrates a result of a search on the front side and the back side using marks made a second time in accordance with an embodiment of the present invention;

[0049] FIG. 17 is a diagram illustrating a bounding box of an OBB with columns of marked points in accordance with an embodiment of the present invention;

[0050] FIG. 18 is a diagram illustrating a process for performing a Boolean intersection operation on two objects in an image space in accordance with an embodiment of the present invention;

[0051] FIG. 19 is a diagram illustrating the ultimately obtained marked 3D region of a suspicious object in accordance with an embodiment of the present invention; and

[0052] FIG. 20 is a diagram illustrating a display of a marked suspicious item in conjunction with the source data in accordance with an embodiment of the present invention.

The implementation of the invention

[0053] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are for illustration only, not limiting the present invention. The following description explains a number of characteristic details in order to provide a better understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention can be practiced without these specific details. In other cases, known structures, materials or methods are not described in detail so as not to impede the understanding of the idea of the present invention.

[0054] Throughout the description of the invention, reference to the terms “one embodiment”, “embodiment”, “one example” or “example” means that specific features, structures or properties described in conjunction with that embodiment or example are included in in at least one embodiment of the present invention. Therefore, the phrase “in one embodiment”, “in an embodiment”, “in one example” or “in an example” in various places throughout the description of the invention may not necessarily refer to the same embodiment or example. In addition, specific features, structures, or properties may be embodied in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, those skilled in the art should understand that the term “and / or” as used herein means any and all combinations of one or more of the listed elements.

[0055] Regarding the impossibility of quickly inserting 3D FTI in the prior art, in accordance with embodiments of the present invention, survey data for the subject being examined is read. At least one 3D FTI is inserted into the 3D image of the examination for the subject being examined, wherein the 3D image of the examination is obtained from the examination data. A selection is made of at least one region in the 3D image of the survey including 3D FTI, or at least one region in the 2D image of the survey including 2D FTI corresponding to 3D FTI, wherein the 2D image of the survey is obtained from 3D survey images or obtained from survey data. In response to the selection, feedback is provided about the 3D image of the survey, including at least one 3D FTI. Using the above solution, it is convenient for the user to quickly mark a suspicious item in the CT image, and feedback is given on whether FTI is present.

[0056] FIG. 1 is a block diagram illustrating a CT system in accordance with an embodiment of the present invention. As shown in FIG. 1, a CT device in accordance with the present embodiment comprises a frame 20, a moving member 40, a controller 50, a computer processing device 60, or the like. The frame 20 comprises a radiation source 10 for emitting x-rays for examination, for example, an x-ray emitter, and a detection and acquisition device 30. The moving element 40 moves the examined baggage 70 to pass through the scanning area between the radiation source 10 and the detection and collection device 30 in the frame 20, while the frame 20 rotates around the direction of movement of the examined baggage 70. Thus, the beam emitted from the radiation source 10 , can pass through the examined baggage 70 to implement a CT scan of the examined baggage 70.

[0057] The detection and acquisition device 30 is, for example, a composite module with an integrated detector and a data acquisition unit, for example a flat detector, for detecting rays passing through an object to be examined to obtain an analog signal, and converting the analog signal to a digital signal, to display the projection data of the examined baggage 70 relative to x-rays. The controller 50 is configured to control various parts of the entire system for collaboration. Computer device 60 data processing is configured to process data collected by the data collection unit, data recovery and output.

[0058] As shown in FIG. 1, a radiation source 10 is placed on one side of the subject to be examined, and a detection and collection device 30 (including a detector and a data collection unit) is placed on the other side of the examined baggage 70 to obtain multi-projection projection data of the examined baggage 70. The data collection unit contains a circuit amplification and data generation, which can operate in the mode of an integrator of electric current or in the pulse counting mode. The detection and acquisition device 30 has a data output cable connected to the controller 50 and the computer data processing device 60 for storing the collected data in the computer data processing device 60 in accordance with the trigger command.

[0059] FIG. 2 is a structural block diagram illustrating the computer data processing apparatus 60 shown in FIG. 1. As shown in FIG. 2, the data collected by the data acquisition unit is stored in the storage device 61 through the interface unit 68 and the bus 64. The configuration information and programs of the computer data processing device are stored in the read-only memory 62 (ROM). The random access memory 63 (RAM) is configured to temporarily store various data during the operation of the processor 66. In addition, a computer program for processing data is also stored in the memory 61. The internal bus 64 is configured to connect a storage device 61, ROM 62, RAM 63, an input device 65, a processor 66, a display device 67, and an interface unit 68 as described above.

[0060] After a user enters an operation command via an input device 65, for example, a keyboard, mouse, or the like, the instruction codes in a computer program control the processor 66 to execute a given data processing algorithm, and after receiving a data processing result, to display the result on the display device 67, such as a liquid crystal display (LCD) display or the like, or outputting the processing result immediately to a hard copy, such as a print.

[0061] FIG. 3 is a structural block diagram showing a controller in accordance with an embodiment of the present invention. As shown in FIG. 3, the controller 50 comprises a control unit 51 configured to control a radiation source 10, a moving member 40, and a detection and acquisition device 30 in accordance with commands from a computer 60; a trigger signal generating unit 52, configured to generate a trigger command to start a radiation source 10, a detection and acquisition device 30, and a moving member 40 for operation under control of a control unit; a first drive device 53 configured to actuate the moving member 40 to move the inspected baggage 70 in accordance with a trigger command generated by the trigger signal generating unit 52 under the control of the control unit 51; and a second drive device 54 configured to drive the frame 20 to rotate in accordance with the trigger command generated by the trigger signal generating unit 52 under the control of the control unit 51. The projection data obtained by the detection and collection device 30 is stored in a tomographic image recovery computer 60 to obtain tomographic images of the examined baggage 70. Then, the computer 60 obtains a DR image of the examined baggage 70 at least from a certain viewing angle from the tomographic image data by software execution and displays a DR image together with a reconstructed three-dimensional image for easy inspection using an image recognition device niy. In accordance with other embodiments, the aforementioned CT imaging system may be a dual energy CT system, that is, the x-ray source 10 in the frame 20 can emit two types of rays, i.e., hard radiation and soft radiation. After the detection and acquisition device 30 detects projection data at different energy levels, the computer data processing device 60 implements a dual energy CT reconstruction to obtain equivalent atomic numbers and electron concentration data from various tomographic images of the examined baggage 70.

[0062] FIG. 4A is a flowchart illustrating a method in an inspection system in accordance with an embodiment of the present invention.

[0063] As shown in FIG. 4A, in step S401, survey data for the subject being examined is read.

[0064] In step S402, at least one 3D FTI is inserted into the 3D image of the examination for the subject being examined, wherein the 3D image of the examination is obtained from the examination data. For example, the data processing device selects one or more 3D images from the FTI database and inserts one or more 3D images into the 3D image of the examination for the subject being examined.

[0065] In step S403, at least one area in the 3D survey image including 3D FTI is received, or at least one region in the 2D survey image including 2D FTI corresponding to 3D FTI, wherein 2D the image of the survey is obtained from a 3D image of the survey or obtained from the survey data. For example, a user uses an input device to activate a check box to select a certain area, or draws a circle so that it includes a certain area in the image displayed on the screen.

[0066] In step S404, a 3D survey image including at least one 3D FTI is provided in response to a selection.

[0067] In some embodiments, the step of receiving a selection of at least one area in a 3D survey image including a 3D FTI, or at least one area in a 2D survey image including a 2D FTI corresponding to a 3D FTI, comprises : Receiving the coordinates of a portion of a 3D survey image or 2D survey image associated with a selection.

[0068] In some embodiments, the step of issuing, in response to a feedback selection of a 3D survey image including at least one 3D FTI, comprises at least one of:

assessing whether at least one selected area includes at least one 3D FTI;

- displaying a dialog to confirm that the 3D image of the examination includes at least one 3D FTI;

- issuing a textual instruction in the interface to confirm that the 3D image of the examination includes at least one 3D FTI;

- highlighting a portion of the 3D survey image or 2D survey image associated with the selection;

- marks of a portion of the 3D survey image or 2D survey image associated with the selection; and

- filling part of the 3D image of the survey or 2D image of the survey associated with the choice, a specific color or pattern.

[0069] For example, at least one spatial parameter of the subject to be examined is calculated in accordance with the survey data, and at least one 3D FTI is inserted into the 3D survey image for the subject to be examined based on the spatial characteristic. In some embodiments, the spatial parameter setting relates to at least one of the position, size, and orientation of the inserted 3D FTI. In addition, the selection of at least one area comprises the selection of a portion of the displayed 3D image of the examination at a certain viewing angle. For example, when 3D-visualizing a 3D image of an examination, information about a point cloud characterizing the object being examined is recorded, and the stage of issuing in response to a feedback selection about a 3D image of the examination, which includes at least one 3D FTI, contains: obtaining a sequence of clusters information about the point cloud of different objects in the subject being examined by separation; determining at least one selected area from a sequence of clusters of information about a cloud of points of different objects based on a given criterion; and evaluating whether at least one selected area includes at least one 3D FTI.

[0070] In other embodiments, the selection of at least one area comprises selecting a portion of the displayed 3D survey image at multiple viewing angles. For example, selecting at least one region comprises selecting a portion of the displayed 3D survey image at two different viewing angles, the two different viewing angles being substantially orthogonal to each other. Here, the transparent areas are removed from the survey data to obtain a hierarchical bounding box from opaque areas in the survey data, and then the scene depth for the hierarchical bounding box is visualized to obtain a depth map on the front side and a depth map on the back side. The step of issuing in response to the selection of feedback about the 3D image of the survey, which includes at least one 3D FTI, comprises: performing a search respectively on the depth map on the front side and the depth map on the back side in accordance with the user-selected area at the first angle a view to form a first bounding box; performing ray tracking when the first bounding box is formed as a texture carrier; performing a search, respectively, on the depth map on the front side and the depth map on the back side in accordance with a user-selected region at a second viewing angle substantially orthogonal to the first viewing angle to form a second bounding box; performing a Boolean intersection operation on the first bounding box and the second bounding box in the image space to obtain a marked area in 3D space as at least one selected area; and evaluating whether at least one selected area includes at least one 3D FTI.

[0071] In some embodiments, the step of inserting at least one 3D FTI into the 3D image of the exam for the subject is comprising: splitting the 3D image of the exam to obtain a plurality of fragments of a 3D image of the subject; calculating the distances between the multiple fragments of the 3D image and their positions; and inserting a 3D FTI based on calculated distances and positions.

[0072] In other embodiments, the step of inserting at least one 3D FTI into the 3D image of the exam for the subject contains: determining transparent parts and opaque parts in the volume data of the subject being examined based on the voxel opacity values; determination of the positions and sizes of the boundaries of the inspected object from opaque parts in volume data; identification of possible insertion positions in transparent areas in the range of borders; and selecting at least one position from the possible insertion positions as at least one selected area in accordance with a predetermined criterion; and evaluating whether at least one selected area includes at least one 3D FTI.

[0073] In other embodiments, the step of inserting at least one 3D FTI into the 3D survey image for the subject to be examined comprises: removing a background image from the 2D survey image to obtain a main 2D image; determination of the 2D position of the insert 2D FTI in the main 2D image; determining the position of the 3D FTI in the 3D image of the survey in the depth direction in the 2D position of the insert; and inserting at least one 3D FTI at a specific position.

[0074] 3D FTIs are inserted as described above, but in some embodiments of the present invention, a 2D FTI corresponding to at least one 3D FTI can be inserted into a 2D image of an examination for a subject.

[0075] In addition, with respect to a problem in the prior art, some embodiments of the present invention provide a quick marking technique for a suspicious item. After the transparent areas are removed from the data, new positions of incidence and exit of the tracked beam are obtained and recorded as a depth map. Based on this, information on the depth of the 2D mark in the voxel space is restored. Two obtained geometric shapes are subject to the operation of Boolean intersection in the image space in order to ultimately get the marked area in 3D space.

[0076] For example, in some embodiments, transparent regions are first removed to quickly obtain a compact hierarchical bounding rectangle from opaque regions in the data, and then the hierarchical bounding rectangle formed above is visualized to obtain a depth map on the front side and a depth map on the back side which are the equalized incidence and fall positions of the tracked beam. Next, a first reading is performed in the direction of the current line of sight, and a depth map is searched on the front side and back side, using columns from the marked points to form a bounding box, for example, a bounding box of OBB. Then, the beam tracking range is updated in accordance with the OBB bounding box formed above, and the second reading is performed at orthogonal viewing angle, which is automatically rotated from the current viewing angle to form a new OBB bounding box. Over the bounding rectangles of the OBB, which are obtained in the previous two steps, the operation of Boolean intersection in the image space is performed to get the final marked area. Ultimately, the suspect area is displayed in conjunction with the source data using the space-based transform function. Using the marking method disclosed in this document, you can quickly and accurately remove transparent areas from CT data, which allows the user to quickly complete the task of marking a suspicious area in a convenient way.

[0077] FIG. 4B is a flowchart illustrating a method for marking a suspicious item in a CT system in accordance with an embodiment of the present invention. After the CT device receives the CT data, first of all, the transparent areas from the CT data are deleted. After transparent areas are quickly removed from the data, the new incidence and exit positions of the beam are recorded as a depth map. In the process of reading, information on the depth of 2D marks in the voxel space is restored by querying the depth information in the depth map in accordance with the 2D marks, and the operation of Boolean intersection in the image space is performed on the two obtained geometric shapes in order to ultimately obtain the marked region in 3D space.

[0078] In step S411, the transparent areas are removed from the CT data obtained by the CT system for inspection based on preliminary integration to obtain a hierarchical bounding box from opaque areas in the CT data.

[0079] 1) Forming a lookup table for pre-integration based on opacity

[0080] The 3D data field processed by volumetric visualization contains discrete data defined in 3D space, and the entire data field is represented by a discrete 3D matrix. Each small rectangle in 3D space represents a scalar value called a voxel. In practical calculations, a voxel can act as a sample point in the 3D data field, and s is the sample scalar value. For the data field s (x), you first need to classify the volume data to specify the colors and attenuation coefficients. The intensity s of the volume data is mapped to color I (s) and attenuation coefficient τ (s) by introducing a conversion function. In the example, the conversion function is determined by gray level data and material data from a dual-energy CT and is also called a 2D color table.

[0081] In bulk imaging, when a 3D scalar field s (x) is selected, the Nyquist sampling rate of the opacity function τ (s (x)) is equal to the maximum Nyquist sampling rate of τ (s) times the Nyquist sampling frequency of the scalar value s (x). Due to the non-linear characteristics of the attenuation coefficient, the Nyquist sampling rate can increase dramatically. To solve the problem that the sampling frequency rises sharply due to non-linear characteristics of the conversion function, a pre-integration method is used. After the pre-integration method is used, it can be quickly determined whether the CT data block represents that the block is transparent.

[0082] Pre-integration mainly consists of two steps. The first step is to create a sample of a continuous scalar field s (x) in the direction of the line of sight. At this point, the conversion function does not affect the sampling rate. The second step is to perform the integration of volumetric visualization over the segment between each two sample points using the search table.

[0083] After the creation of the s (x) sample, the integration of volumetric visualization over each segment is performed. The integration process is performed through a lookup table. There are three parameters in the search table, including the starting point S _f = s (x (i (d)) of the segment, the ending point S _b = s (x ((i + 1) · d)) of the segment and the length d of the segment. Assume that the segment length d is constant, and only two parameters S _f and S _b need to be taken into account during the calculation using the lookup table.

[0084] The opacity of the i-th segment can be represented as:

[0085] An integration function is introduced into the above equation for acceleration, and then the opacity α _i = α (s _f , s _b ) can be rewritten as:

Where

[0086] 2) Removing the transparent region based on the octree

[0087] The Octree is a tree data structure for describing 3D space. FIG. 5 is a schematic diagram illustrating an octave subdivision algorithm. Each octree node represents a volume element in the form of a cube. Each node has 8 subnodes, and the sum of all volume elements represented by 8 subnodes is equal to the volume of the parent node. Illustrated in FIG. The 5th octree tree includes 8 nodes: ulf, urf, ulb, urb, llf, lrf, llb and lrb. When spatial data is subdivided using the octree encoding algorithm, suppose that the represented body V can be placed in a sufficiently large cube C, and then the octree tree for the body V relative to the cube C can be defined in the following recursive way. Each octree node corresponds to a subcube C, and the root node of the octree corresponds to C. If V = C, then the octree V has only a tree node; and if V ≠ C, then C is divided equally into 8 subcubes, and each subcub corresponds to a subnode of the root node. If the subcube is not completely empty or if it is not completely occupied by body V, then the subcube is additionally divided equally into 8 parts, and thus the corresponding node has 8 subnodes. Recursive evaluation and separation are carried out until the cube corresponding to the node is completely empty, or completely occupied by body V, or has received a size equal to the predetermined size of the cube.

[0088] Volume data is subdivided into levels according to a predetermined sheet size. When the data field passes, the maximum s _max and minimum s _{min of} all voxels in the sub-block corresponding to the sheet, the axial bounding box corresponding to the sub-rectangle, and its volume value are statistically calculated. Then the nodes are combined up the levels to build an octree. The subdivision of the octree is shown in FIG. 5.

[0089] According to an embodiment of the present invention, an octree is passed to recursively set the visibility state of each node at each level. For a node that is not a leaf, the state can be transparent, partially transparent, or opaque. The state of a node is determined by the states of the subnodes included in this node. If all subnodes are transparent, then the current node is transparent; if all subnodes are opaque, then the current node is opaque; and if part of the subnodes is transparent, then the current node is translucent. For a sheet, the state can only be transparent or opaque. The visibility state of a sheet is obtained by requesting opacity. In particular, when the octree is built, the minimum and maximum values of the gray level (s _min , s _max ) for each subunit are stored, and the opacity α of the current subunit is quickly obtained using the aforementioned opacity request function α (s _f , s _b ). If α≥α _ε , then the current sheet is opaque, where α _ε is a predetermined opacity threshold. As shown in FIG. 6, the remaining opaque parts are illustrated after the transparent blocks are removed, the large cubic block representing the size indicated by the source data.

[0090] In step S412, the scene depth for the hierarchical bounding box is visualized to obtain a depth map on the front side and a depth map on the back side. At step S413, a search is performed respectively on the depth map on the front side and the depth map on the back side, using the marks made by the user, in the direction of the line of sight to form the first bounding box. FIG. 7 is a diagram illustrating a depth map on the front side according to an embodiment of the present invention. FIG. 8 is a diagram illustrating a depth map on the rear side in accordance with an embodiment of the present invention.

[0091] For 3D rendering, you need to use 3D mode as the medium for the 3D texture, and then the 3D texture is mapped to the model using texture coordinates. Then a ray is drawn from point of view to point on the model. A ray passing through the model space is equivalent to a ray passing through a three-dimensional texture. Thus, the determination of the positions of the incidence and exit of the tracked beam becomes the problem of solving the intersection of the beam with the carrier volume texture. As shown in FIG. 7, a depth map on the front side is obtained by rendering a depth map of the scene for the hierarchical bounding box obtained above and then removing sections with relatively large depths from the rendered depth map of the scene, the color value of each pixel on the depth map on the front side representing the distance from the point of view to the point closest to the point of view in some direction. As shown in FIG. 8, a depth map on the back side is obtained by visualizing a depth map of the scene for the hierarchical bounding box obtained above and then removing sections with relatively small depths from the rendered depth map of the scene, the color value of each pixel on the depth map on the back side representing the distance from the point of view to the point farthest from the point of view in some direction.

[0092] FIG. 9 is a diagram illustrating a ray tracking process in accordance with an embodiment of the present invention. The basic ray tracking process is as follows. In particular, a beam is emitted from each pixel of the image in a fixed direction, and then passes through the entire sequence of images. In this process, a sequence of images is selected and classified to obtain color values. Color values are accumulated in accordance with the ray absorption model until the ray passes through the entire sequence of images. The color value ultimately obtained is the color of the rendered image. Illustrated in FIG. 9, the ray tracking plane is the aforementioned “image”.

[0093] Ultimately, using the ray tracking, a 2D image is obtained, and thus, it is not possible to reconstruct the voxel depth information through which the monitored pixel ray passes. To complete the reading of the areas in the voxel space, as shown in FIG. 10, a suspicious area is marked on the beam tracking plane, and the noted result is shown in FIG. 11. To restore information about the depth in voxel space from the marked result, the marked event is discretized into columns of points, and a search is performed on the depth map on the front side and the depth map on the back, respectively, to obtain the result of the projection of the marked area onto those depth maps. FIG. 12 is a diagram illustrating a search process on the front side and the back side using user marks in accordance with an embodiment of the present invention. In this case, one 2D marking operation on the screen image is restored to 3D markings in voxel space.

[0094] After performing one marking operation, the range of the suspicious area is still relatively large. To further reduce the suspicious area, you need to calculate the hierarchical bounding box OBB corresponding to the columns of the marked points in the voxel space.

[0095] The fundamental idea of the bounding box method is to replace various complex geometric shapes with simple geometric shapes. First of all, crude detection is performed over the bounding rectangles of an object. Only when bounding rectangles intersect can geometric shapes bounded by bounding rectangles intersect. When bounding rectangles do not intersect, geometric shapes bounded by bounding rectangles must not intersect. Thus, it is possible to exclude a large number of geometric shapes and geometric parts that cannot intersect in order to quickly find geometric parts that intersect. There are several types of bounding rectangles, namely the bounding rectangle (AABB) aligned with the coordinate axes, the bounding sphere, the bounding rectangle of the OBB in any direction, and the more general bounding box k-dop. After the envelope is compact and the computational cost of the various bounding boxes, the bounding box OBB is selected to calculate the columns from the marked points. The fundamental point in calculating the bounding box OBB is to find the optimal direction and determine the minimum size of the bounding box that bounds the object in that direction. The position and direction of the bounding box are calculated using statistics of the first-order moment (average value) and second-order moment (covariance matrix). Suppose that the ith vertex is p ⁱ and the number of vertices bounded by a bounding box is n. The center position of the bounding box is as follows:

[0096] Assume that the covariance matrix is represented as:

[0097] Elements of the covariance matrix are represented as:

является вектором 3×1, а

.Where

is a 3 × 1 vector, and

.

[0098] The eigenvectors of the covariance matrix are solved numerically and combined. Since C is a real symmetric matrix, the eigenvectors of the matrix C are orthogonal to each other and can be used as the direction axes of the bounding box. The vertices of the constrained geometric shape are projected on the direction axis to find projective intervals on the respective direction axes. The lengths of the corresponding projective intervals usually represent the size of the bounding box to be solved. FIG. 13 is a diagram illustrating a bounding box of an OBB with columns of marked points in accordance with an embodiment of the present invention.

[0099] In step S414, the beam is cast when the first bounding box is formed as a texture carrier. In step S415, the search is performed respectively on the depth map on the front side and the depth map on the back side, using the marks made by the user in the direction orthogonal to the direction of the line of sight to form a second bounding box.

[00100] 1) Updating the ray tracking range

[00101] As shown in FIG. 14, after the range of the suspicious region is determined, parts outside the region are removed from the display, and the beam is cast using the formed bounding rectangle OBB as a new volumetric texture medium.

[00102] 2) The second reading after turning the viewing angle

[00103] FIG. 15 is a diagram illustrating a result of marking a second time in the orthogonal direction in accordance with an embodiment of the present invention. FIG. 16 illustrates a result of a search on the front side and the back side using marks made a second time in accordance with an embodiment of the present invention. FIG. 17 is a diagram illustrating a bounding box of an OBB with columns of marked points in accordance with an embodiment of the present invention. In step S416, a Boolean intersection operation is performed on the first bounding box and the second bounding box in the image space to obtain a marked area in 3D space. FIG. 18 is a diagram illustrating a process for performing a Boolean intersection operation on two objects in an image space in accordance with an embodiment of the present invention.

[00104] In order to speed up the preparation of the intersected area between the two bounding rectangles of the OBB, the structural block geometry (CSG) method is used for calculation. The CSG model can be visualized using OpenGL in two ways. In one direction, the CSG model is visualized based on the space of objects. In particular, the CSG model is converted directly to a set of polygons, and then rendered using OpenGL. A typical way for this solution is to convert the CSG model to the outline representation model (B-rep). However, the transformation of the model leads to low efficiency and is inconvenient for dynamic change. In the other direction, the CSG model is rendered based on the image space that is used in the present invention.

[00105] The intersection operation is performed in the image space without changing the model, and the operation is performed dynamically for each frame to decide which surface (s) should be displayed and which surface (s) should be hidden or deleted. The intersection operation in CSG is achieved using a stencil buffer (template) in OpenGL. In the idea of tracking rays, when the surface of an object is projected onto the screen, the number of intersections of pixels on its surface relative to other surfaces is calculated.

[00106] Using the above operations, two cubes are obtained. The search for intersections between two cubes essentially consists in finding parts of the surface of one cube in the volume of another cube. In the intersection search process, the front surface and the rear surface of any given constituent object are visualized accordingly in the respective channels. In particular, in each visualization process, the current surface is first transferred to the depth buffer, and then parts of the current surface in other objects are determined using other objects together with the operation of the stencil plane.

[00107] Here, using parity, it is judged whether a point is in a given space of an object. Theoretically, using parity, it is possible to evaluate whether any point in space is in a given volume. However, since the depth buffer in OpenGL can only store one depth value for each pixel, the process of parity when visualizing the intersection between subject A and subject B is as follows. First of all, parts A to B are found and visualized, and then parts B to A are found and visualized. At the same time, the front side of A is visualized B. To get the front side of B in A, first of all, the pixels in the depth buffer are re-rendered, which are closed front side B. The reason is that all parts A are in the depth buffer after the above operations, and parts A outside B can obscure parts B that should be visible. After the depth values of B are corrected in the depth buffer, parts of the front side of B in A are located and visualized in the same way as above, and a description of this will be omitted here. FIG. 19 is a diagram illustrating an ultimately obtained 3D region of a suspicious object in accordance with an embodiment of the present invention.

[00108] In step S417, the marked area in the 3D space is connected and displayed together with the CT data. For example, after a suspicious area has been read, you need to connect the suspicious area with the source data and display the suspicious area together with the source data with a higher priority of visibility. As can be seen from FIG. 18, the final suspicious area may not have a regular cuboid shape. A space-limited transform function is used here. A one-dimensional search texture is formed in accordance with the dimension of the volume data using the line-by-line construction algorithm, and each texel stores information about whether the corresponding spatial position is in the bounding box of the suspicious area. The final result of the combined visualization is shown in FIG. twenty.

[00109] Moreover, when a TIP is inserted into the CT data, it is necessary not only to ensure that the inserted TIP is in the range of borders, but also that the inserted image also does not cover the original objects in the borders. In addition, the real-time requirement for the algorithm is also an important factor to consider. In accordance with some embodiments, transparent regions and opaque regions in the volume data are determined according to opacity, and portions of the empty boundary regions, the volumes of which meet the requirements, are selected as possible insertion positions, and finally, the insertion position with a given masking level in according to the distance from the position to the observation plane and the number of objects around that position.

[00110] For example, a pre-integration lookup table is generated based on opacity to quickly identify transparent areas and opaque areas in volume data. Then, an opaque octree is constructed for volume data to determine the position and size of the boundaries in the CT data. Then, the transparent octree is constructed for volume data to obtain the areas within the borders that are available for insertion, and the transparent octree only statistically calculates the transparent parts in the data area and completely eliminates the opaque parts. Parts of the transparent regions whose volumes meet the requirements of the insert are selected as possible insert positions. The final position of the insert is determined in accordance with the specified level of masking of the insert.

[00111] Using the FTI solutions disclosed herein, it is possible to quickly insert into CT data, and it can be ensured that the insertion position is within borders while the inserted image does not cover the original objects within the boundaries. The masking level of the insert can be set using the parameter (s), and the algorithm can provide real-time performance.

[00112] In the foregoing detailed description, various embodiments of a method and apparatus for marking a suspicious item in a CT scan system are set forth using diagrams, flowcharts, and / or examples. If such a circuit, a flowchart of an algorithm and / or an example includes one or more functionalities and / or operations, those skilled in the art will understand that each functionality and / or operation in a flowchart, a flowchart of an algorithm or in the example, can be implemented individually and / or together through a wide range of structures, hardware, software, firmware, or essentially any combination thereof. In one embodiment, several parts of the subject matter described in the embodiments of the present invention can be implemented by application specific integrated circuit (ASIC), field programmable gate array (FPGA), digital signal processor (DSP), or other integrated formats. However, those skilled in the art will appreciate that some aspects of the embodiments disclosed herein may be fully or partially implemented equally in integrated circuits, in the form of one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (for example, as one or more programs running on one or more microprocessors), as firmware, or substantially in the form of any combination thereof, and that the circuit design and / or writing the code for the software and / or firmware would be quite capable of skill in the art in connection with this invention. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described in this document can be distributed in various forms as a software product, and that illustrative embodiments of the subject matter described herein are applied regardless of the particular types of signal carriers used for the actual implementation of the distribution. Examples of signal carriers include, but are not limited to, recordable media, such as a floppy disk, hard disk drive, compact disc (CD), digital versatile disk (DVD), digital magnetic tape, computer memory or the like, and transmission media for example, digital and / or analog communications (e.g., fiber optic cable, waveguide, wireline, wireless, or the like).

[00113] Although the present invention has been described with respect to several embodiments, it will be apparent to those skilled in the art that the terms are used to illustrate and explain, and not to limit. The technology disclosed in this document can be implemented in various forms without departing from the spirit of the present invention. It should be understood that the options for implementation are not limited to the above details and should be widely interpreted within the spirit and scope defined by the following claims. Therefore, all modifications and alternatives falling within the scope of the claims or their equivalents should be covered by the attached claims.

Claims (16)

1. A method in a computed tomography (CT) system for inspection, comprising the steps of:
read survey data for the subject;
insert at least one fictitious three-dimensional (3D) image of the dangerous object (FTI) into the 3D image of the examination for the subject being examined, wherein the 3D image of the examination is obtained from the survey data;
take a selection of at least one region in the 3D image of the survey, including 3D FTI, or at least one region in the two-dimensional (2D) image of the survey, including a 2D FTI corresponding to the 3D FTI, wherein the 2D image of the survey is obtained from 3D images of the survey or obtained from survey data; and
in response to said selection, feedback on the 3D image of the survey including at least one 3D FTI is provided. 2. The method of claim 1, wherein the step is to select at least one area in the 3D image of the exam including 3D FTI, or at least one area in the 2D image of the exam including 2D FTI corresponding to 3D FTI, comprises the step of:
take the coordinates of the portion of the 3D survey image or 2D survey image associated with the selection. 3. The method of claim 1, wherein the step of providing feedback on the 3D image of the survey including at least one 3D FTI in response to said selection comprises at least one of the steps of:
assessing whether at least one selected region includes at least one 3D FTI;
displaying a dialog to confirm that the 3D image of the examination includes at least one 3D FTI;
issuing a text indication in the interface to confirm that the 3D image of the examination includes at least one 3D FTI;
extracting a portion of the 3D survey image or 2D survey image associated with the selection;
marking the portion of the 3D survey image or 2D survey image associated with the selection; and
fill in the portion of the 3D survey image or 2D survey image associated with the selection, a specific color or pattern. 4. The method according to claim 1, wherein at least one spatial parameter of the subject to be examined is calculated in accordance with the survey data, and at least one 3D FTI is inserted into the 3D image of the survey for the subject to be examined based on the spatial characteristic. 5. The method according to claim 4, wherein the spatial characteristic parameter refers to at least one of the position, size and orientation of the inserted 3D FTI. 6. The method according to claim 1, wherein selecting at least one area comprises the step of selecting a portion of the displayed 3D survey image at a certain viewing angle. 7. The method according to claim 6, in which, when 3D-visualizing a 3D image of an examination, information about a point cloud characterizing the subject to be examined is recorded, and the stage at which feedback on the 3D image of the examination including at least one 3D FTI, comprises the steps of:
obtaining a sequence of clusters of information about a point cloud for various objects in the subject being examined by separation;
determining at least one selected region from a sequence of clusters of point cloud information for various objects based on a predetermined criterion; and
assessing whether at least one selected region includes at least one 3D FTI. 8. The method according to claim 1, wherein selecting at least one area comprises selecting a portion of the displayed 3D survey image from a plurality of viewing angles. 9. The method of claim 8, wherein selecting at least one area comprises selecting a portion of the displayed 3D survey image at two different viewing angles, the two different viewing angles being substantially orthogonal to each other, and the transparent areas are removed from survey data to obtain a hierarchical bounding box from opaque areas in the survey data, and then visualize the scene depth for the hierarchical bounding box to obtain a depth map on the front side and the depth map on the back side, and the step of in response to said selecting give feedback on the 3D-image survey includes at least one 3D FTI, comprising the steps of:
performing a search, respectively, on the depth map on the front side and the depth map on the back side in accordance with the region selected by the user at a first viewing angle to form a first bounding box;
performing ray tracking when the first bounding box is formed as a texture carrier;
searching, respectively, on a depth map on the front side and a depth map on the back side in accordance with a user-selected region at a second viewing angle substantially orthogonal to the first viewing angle to form a second bounding box;
performing a Boolean intersection operation on the first bounding box and the second bounding box in the image space to obtain the marked area in 3D space as at least one selected area; and
assessing whether at least one selected region includes at least one 3D FTI. 10. The method of claim 1, wherein the step of inserting at least one 3D FTI into the 3D image of the exam for the subject being examined comprises the steps of:
sharing a 3D image of the survey to obtain a plurality of fragments of a 3D image of the subject;
calculate the distance between the multiple fragments of the 3D image and their position; and
insert 3D FTI based on calculated distances and positions. 11. The method of claim 1, wherein the step of inserting at least one 3D FTI into the 3D image of the exam for the subject being examined comprises the steps of:
determining the transparent parts and the opaque parts in the volume data of the subject to be examined based on the opacity values of the voxels;
determine the position and size of the boundaries of the examined subject from opaque parts in the volume data;
determine the possible position of the insert in transparent areas in the range of borders; and
at least one position is selected from the possible insertion positions in accordance with a predetermined criterion in order to insert at least one 3D FTI in at least one selected position. 12. The method of claim 1, wherein the step of inserting at least one 3D FTI into the 3D image of the examination for the subject being examined comprises the steps of:
removing the background image from the 2D survey image to obtain a basic 2D image;
determine the 2D position of the insert 2D FTI in the main 2D image;
determining the position of the 3D FTI in the 3D image of the survey in the depth direction in the 2D position of the insert; and
insert at least one 3D FTI at a specific position. 13. The method of claim 1, further comprising the step of: inserting a 2D FTI corresponding to at least one 3D FTI into the 2D image of the exam for the subject being examined. 14. System of computed tomography (CT) for inspection, containing:
CT scan device configured to obtain survey data for the subject being examined;
a storage device configured to store survey data;
a display device configured to display a three-dimensional (3D) image of the examination and / or two-dimensional (2D) image of the examination for the subject being examined, wherein the 3D image of the examination is obtained from the examination data and the 2D image of the examination is obtained from the 3D image of the examination or examination ;
a data processing device configured to insert at least one fictitious 3D image of a dangerous object (FTI) into a 3D image of the examination for the subject being examined; and
an input device configured to receive a selection of at least one area in a 3D survey image including 3D FTI, or at least one area in a 2D survey image including 2D FTI corresponding to 3D FTI,
moreover, the data processing device is further configured to provide, in response to said selection of feedback, a 3D survey image including at least one 3D FTI. 15. The CT scan system according to claim 14, wherein the data processing device is further configured to calculate at least one spatial parameter of the subject to be examined in accordance with the survey data and to insert at least one 3D FTI into the 3D survey image for the subject being examined based on the spatial characteristic parameter. 16. A CT scan system according to claim 14, wherein the spatial characteristic parameter refers to at least one of the position, size and orientation of the inserted 3D FTI.

Images